Tech giant Google is not one to shy away from revolutionary new uses of artificial intelligence, and the newest out of the company's wheelhouse is an AI program that's able to predict a patient's risk of heart disease by looking for signs of a number of key risk factors in their eyes. Eye factors like blood vessels and lines in the ocular disc can give away things like that to the very well-trained eye, and this AI was very well-trained to look for discrepancies of just those sorts. "Given the retinal image of one patient who (up to 5 years) later experienced a major CV event (such as a heart attack) and the image of another patient who did not, our algorithm could pick out the patient who had the CV event 70 per cent of the time". Typically, this assessment includes examining risk factor such as age, sex, smoking, blood pressure, and cholesterol, as well as taking into account whether the patient has another disease associated with increased risk of cardiovascular issues, such as diabetes.
Luke Oakden-Rayner, a medical researcher at the University of Adelaide who specializes in machine learning analysis, told The Verge, "They're taking data that's been captured for one clinical reason and getting more out of it than we now do". More research would resolve whether the model needs to be adjusted for larger or smaller photos, and a larger data set than what the researchers used is more appropriate for deep learning. Information included the results of the eye screening and General health information.
According to the Centers for Disease Control and Prevention, around 610,000 people die of heart disease in the United States every year, and 370,000 of these deaths are due to coronary heart disease (CHD).
Verily's head of cardiovascular health innovations, Michael McConnell, said it is promising but early research. In biology the rear interior wall of the eye is full of blood vessels that reflect the body's overall health.
With this in place, doctors can detect a patient's cardiovascular risk, as it doesn't require a blood test.
Google also used some attention techniques to find out how the algorithm was making its prediction. In future studies, the researchers said they plan to explore the effects of interventions such as lifestyle changes or medications on risk predictions.
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